Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Models

TL;DR

This paper introduces a unified control framework for humanoid robots that enhances push recovery during walking by integrating arm use and environment interaction, improving robustness against external disturbances.

Contribution

It combines SRB-MPC with HLIP dynamics and environmental cues to enable humanoids to recover from pushes using arms and walls, a novel integration for robust locomotion.

Findings

Robot recovers from 100N pushes for 0.2s during walking

Improved perturbation rejection and tracking performance

Validated robustness with angled walls and multi-directional pushes

Abstract

Push recovery during locomotion will facilitate the deployment of humanoid robots in human-centered environments. In this paper, we present a unified framework for walking control and push recovery for humanoid robots, leveraging the arms for push recovery while dynamically walking. The key innovation is to use the environment, such as walls, to facilitate push recovery by combining Single Rigid Body model predictive control (SRB-MPC) with Hybrid Linear Inverted Pendulum (HLIP) dynamics to enable robust locomotion, push detection, and recovery by utilizing the robot's arms to brace against such walls and dynamically adjusting the desired contact forces and stepping patterns. Extensive simulation results on a humanoid robot demonstrate improved perturbation rejection and tracking performance compared to HLIP alone, with the robot able to recover from pushes up to 100N for 0.2s while walking at commanded speeds up to 0.5m/s. Robustness is further validated in scenarios with angled walls and multi-directional pushes.